code: def _get_init_modes_for_test(self, cpu_offload):
      modes = [CUDAInitMode.CUDA_AFTER, CUDAInitMode.CUDA_BEFORE]
      # Note that CUDAInitMode.CUDA_NEVER works currently only with CPU
      # offload as we explicitly bring the param back to CUDA device. In
      # general, it will not work since we try to all_gather p.data which is
      # on CPU but NCCL only supports GPU.
      if cpu_offload.offload_params:
          modes.append(CUDAInitMode.CUDA_NEVER)
  
      return modes
  
imports: import copy
  import functools
  import itertools
  import sys
  import unittest
  from typing import List, Optional
  import torch
  from torch import distributed
  from torch.cuda.amp.common import amp_definitely_not_available
  from torch.distributed.fsdp import CPUOffload, MixedPrecision
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel, ShardingStrategy
  from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
  from torch.distributed.fsdp.wrap import ModuleWrapPolicy
  from torch.nn import TransformerDecoderLayer, TransformerEncoderLayer
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import CUDAInitMode, DummyProcessGroup, FSDPInitMode, FSDPTest, NestedWrappedModule, NonUniformReqGradNWM, subtest_name, TransformerWithSharedParams
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests, TEST_WITH_DEV_DBG_ASAN, TestCase
label_desc: Use image node_id 1 for calling the TestShardedGradScalerParityWithDDP obj's underlying member method code with example usage: obj._get_init_modes_for_test(cpu_offload) and returns: modes